Evidence map›Paper›PMID 41735394›Full record

ArticleScientific reports2026

Human versus artificial intelligence in oral pathology diagnosis: a comparative study of ChatGPT, Grok, and MANUS.

Abdullah F Alshammari, Ahmed A Madfa, Bassam Ali Anazi

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Abdullah F AlshammariDepartment of Basic Dental and Medical Science, College of Dentistry, University of Ha'il, Ha'il, Kingdom of Saudi Arabia. Abd.alshamari@uoh.edu.sa.
Ahmed A MadfaDepartment of Restorative Dental Science, College of Dentistry, University of Ha'il, Ha'il, Kingdom of Saudi Arabia. ahmed_um_2011@yahoo.com.
Bassam Ali AnaziDepartment of Basic Dental and Medical Science, College of Dentistry, University of Ha'il, Ha'il, Kingdom of Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) integration in diagnostic medicine has advanced accuracy and efficiency, particularly in pathology. This study assessed the diagnostic performance of three large language models (LLMs)-ChatGPT (GPT-4-turbo), Grok (xAI), and MANUS-in interpreting histopathology slides of oral lesions. A comparative diagnostic study was conducted using 100 high-resolution slides representing diverse oral pathologies. Images were sourced from a validated textbook and reviewed by two board-certified oral pathologists who provided consensus diagnoses. Each slide was analysed twice by the three AI models using standardized prompts. Diagnostic accuracy, intra-model consistency, inter-model concordance, and agreement with human experts were evaluated using descriptive statistics, Cohen's kappa, McNemar's test, and chi-square analysis. All AI models demonstrated high diagnostic accuracy. In the second round, Grok achieved the highest accuracy (97%), followed by MANUS (96%) and ChatGPT (94%). ChatGPT showed the highest intra-model consistency (κ = 0.918), while MANUS and Grok displayed substantial agreement (κ = 0.790 and 0.740). Expert pathologists achieved 98% accuracy. Comparisons between AI models and human diagnoses showed moderate to substantial agreement, with MANUS most aligned with experts. Most misclassifications occurred in histologically ambiguous cases, with no significant differences between AI models. Multimodal LLMs demonstrated strong diagnostic capabilities, consistency, and alignment with expert reasoning in oral histopathology interpretation. Grok was the most accurate, ChatGPT the most consistent, and MANUS the most expert-aligned. These findings support AI integration into digital pathology for diagnostic support, education, and quality assurance, with further validation in clinical datasets recommended.

Indexed as

Artificial IntelligencePathology, OralGenerative Artificial IntelligenceHumansLarge Language ModelsArtificial intelligenceDiagnostic accuracyDigital pathologyLarge language modelsOral pathology

Identifiers

PMID41735394
PMCPMC13043823

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.